Method and system for conducting ecommerce transactions in messaging via search, discussion and agent prediction

ABSTRACT

A computer-implemented method of using the Internet to promote goods and services and connect merchants with potential purchasers in chat groups who wish to obtain suitable sources of goods and services is provided, wherein a plurality of users each have a computer device provided with chat application software and software for accessing and interactively communicating via a computer network with a server provided with a search engine for searching the Internet. Users initiate a chat conversation among a group of users. One of the users invokes a search application using the search engine. The user conducts a search of the Internet for products or services, reviews the results of the search, selects a product or service located by the search, and shares the selected search result with the chat conversation. One of the users can order the selected product or service as part of the process.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims the benefits, under 35 U.S.C.§119(e), ofU.S. Provisional Application Ser. No. 61/994,625 filed May 16, 2014entitled “Method and System for Generating Business Referrals from ChatDiscussion Groups” which is incorporated herein by this reference.

TECHNICAL FIELD

The invention relates to methods of using the Internet to promote goodsand services and connect merchants with potential purchasers. Moreparticularly the invention relates to methods of using software agentsto assist chat groups in obtaining suitable sources of goods andservices.

BACKGROUND

Various methods have been developed for directing online shoppers tomerchant web sites of interest and rewarding the referring party for thereferral. For example U.S. Pat. No. 6,029,141 to Amazon.com, Inc.discloses a method of Internet-based referral wherein associates marketproducts of a merchant on their websites which customers may thenpurchase through a referral link on the associate website which takesthe customer to the merchant website. If the customer purchases theproduct then the associate is paid a referral fee.

Similarly searches on map sites such as Google maps will providerecommendations to the searching party as to a restaurant, hotel etc. inthe vicinity of the location searched. A large proportion ofcommunications by smartphone now consist of smartphone instant messagingor SMS messaging. To date however businesses have not taken advantage ofgroup discussions such as chat messaging groups where such groups aresearching for products or services on a group basis, nor have they takenadvantage of contextual information which can affect the currentinclination of such groups to purchase goods or services.

Google has created an online knowledge base called Knowledge Graph whichuses semantically organized information to enhance its search results.Interest graphs are also used as online representations of a particularindividual's specific interests.

A “software agent” is a computer program that acts independently toperform tasks for its principal, whether a person or another computerprogram.

Software agents have been used for many years in such roles as shoppingor buyer agents, monitoring and surveillance agents, data mining agentsand communication agents. Software agents add to the user's capabilityto obtain useful information.

Programmatic advertising is a term for the buying of impressions onsmartphone apps or websites, known as “programmatic direct”. Buying canbe triggered automatically through a predefined set of conditions inmuch the same way that stock trading is done. Since smartphones generatecontextual data about their users, it would be desirable to use suchcontextual awareness and programmatic buying of ads to allow companiesto target users on mobile apps and websites for advertising.

The foregoing examples of the related art and limitations relatedthereto are intended to be illustrative and not exclusive. Otherlimitations of the related art will become apparent to those of skill inthe art upon a reading of the specification and a study of the drawings.

SUMMARY

The following embodiments and aspects thereof are described andillustrated in conjunction with systems, tools and methods which aremeant to be exemplary and illustrative, not limiting in scope. Invarious embodiments, one or more of the above-described problems havebeen reduced or eliminated, while other embodiments are directed toother improvements.

An embodiment provides a computer-implemented method of using theInternet to promote goods and services and connect merchants withpotential purchasers in chat groups who wish to obtain suitable sourcesof goods and services, wherein a plurality of users each have a computerdevice provided with chat application software and software foraccessing and interactively communicating via a computer network with aserver provided with a search engine for searching the Internetcomprising:

-   a) said users initiating a chat conversation among a group of users;-   b) one of said users invoking a search application for utilizing    said search engine;-   c) said one of said users conducting a search of the Internet for    products or services;-   d) said one of said users reviewing the results of said search of    the Internet for products or services and selecting a product or    service located by said search;-   e) said one of said users injecting said selected search result into    said chat conversation;-   f) said one of said users or others of said users ordering said    selected product or service.

A further embodiment provides a consumer-oriented software agentaccessed from any mobile device, tablet, smart device, laptop or desktopcomputer. The agent curates an understanding of each user's past,current and possible future states and needs in the form of a user graphwhich creates and tracks the constantly evolving and changing Userstate. To do this a novel form of notation can be used for communicationbetween User and Agent, such as a Simple Knowledge Graph Notation. Oncethe Agent has built a User Graph, the agent can assist the user inlocating useful information and predicting the user's future states andneeds. The agent then can also assist chat groups in obtaining suitablesources of goods and services by combining the user graphs of the usersin a chat group into a group graph.

In addition to the exemplary aspects and embodiments described above,further aspects and embodiments will become apparent by reference to thedrawings and by study of the following detailed descriptions.

BRIEF DESCRIPTION OF DRAWINGS

Exemplary embodiments are illustrated in referenced figures of thedrawings. It is intended that the embodiments and figures disclosedherein are to be considered illustrative rather than restrictive.

FIG. 1a is a schematic diagram illustrating the network or system usedto carry out the invention;

FIG. 1b-g are screen shots of a mobile application for carrying out afirst embodiment of the invention;

FIG. 2 is a schematic diagram illustrating the knowledge density in auser graph as a function of time;

FIG. 3 is a schematic diagram illustrating a User State Graph slice withsubgraph display;

FIG. 4 is a schematic diagram illustrating a User State Graph Processing& Analysis for Inference and Action;

FIG. 5 is a schematic diagram illustrating a Group Chat Assembly;

FIG. 6 is a screen shot illustrating a contact list;

FIG. 7 is a screen shot illustrating a group chat session;

FIG. 8 is a screen shot illustrating a group chat session;

FIGS. 9a and 9b are a schematic diagram illustrating Creation of a GroupState Graph from User Graphs;

FIG. 10 is a schematic diagram illustrating creation and curation of theGroup Graph; and

FIG. 11 is a schematic diagram illustrating a Conversation Subgraph andAgent Actions.

DESCRIPTION

Throughout the following description specific details are set forth inorder to provide a more thorough understanding to persons skilled in theart. However, well known elements may not have been shown or describedin detail to avoid unnecessarily obscuring the disclosure. Accordingly,the description and drawings are to be regarded in an illustrative,rather than a restrictive, sense.

For purposes of this application the following definitions apply:

A “User Graph” is a real time dynamic graph formed from contextual datarelating to a specific user which is built from data collected from Userchats, searches, existing interest graphs such as the User's Facebookinterest graph, smartphone apps, web browsing and other available Userbehavioral data in which the nodes consist of objects, people, places,things, concepts etc. and an edge defines a relation between thevertices connected by the edge. It can be visually represented as agraph consisting of nodes and edges. It can consist of a number ofsubgraphs.

A “User State Graph” is the state of a User Graph at a given point intime.

A “Group Graph” is a real time dynamic graph formed from the User Graphsrelating to a number of specific users who form a chat group.

A “Group State Graph” is the state of a Group Graph at a given point intime.

“Agent” is a software agent which maintains the User and Group Graphs.

With reference to FIG. 1 a, the method of the invention is carried outby users 10 via a plurality of user computer terminals, whether desktop,tablet, laptop, smart phone, other mobile device or the like, andprovided with application software to access Agent server 22 via Agentweb server 12 via the Internet 14 and a social network hosting server24. A vendor advertising agent server 16 also accesses Agent web server12 via the Internet 14.

In a first basic embodiment of the invention illustrated in FIG. 1b -1g, a group of users 10 participate in a chat conversation oversmartphones 10 provided with a chat application such as FacebookMessenger, and a search agent application according to the invention.Fig. lb illustrates a screen shot from the smartphone of a participantin a chat session among the group “Calgary Troublemakers” on FacebookMessenger. The control panel at the bottom of the screen includes anextra icon, shown as a magnifying glass 11, which is a search button.During the chat conversation, a user 10 may touch the search button 11to send a request to the operating system, which invokes the searchagent (“Max”) screen shown in FIG. 1 c. The user can then enter searchterms in box 13. The device's GPS may automatically enter a geographiclimiter for the search in box 15, or the user can enter a differentgeographic location. A search engine (e.g. Google or the system's ownsearch engine) then carries out a web search based on the key words andreturns the search results as shown in Fig. lc, preferably filtered intodifferent categories, one of the examples in this case being “Products”.FIG. 1d illustrates a screen shot when the user selects the “Product”category to review the search hits. Each search hit is displayed as acard or tile 17. The source of the search hit is shown by the logo at19. By tapping the card 17 on the smartphone screen, the screen shown inFIG. 1e is invoked. At the screen shown in FIG. 1 e, the user cancompose a message to insert the selected search result into the chatgroup, or directly order the product. A comment about the selectedproduct can be entered in box 21. Selecting the order button at 25 takesthe user to the source of the search hit to order the product.

Still with reference to FIG. 1 e, by selecting the “Send” button 27 theuser can inject the selected product card into the chat group or send itto other recipients. Tapping the “Send” button 27 takes the user to thescreen shown in Fig. if where the user can select the recipients to besent the product card. Tapping the arrow at 29 will inject the productcard and comment as shown in FIG. 1e back into the chat conversation inFIG. 1b from which the search function was invoked, or the user canelect to send the card as a separate message to each selected recipientby tapping the “Send Separately” button 31. FIG. 1g illustrates theproduct card having been injected into the chat group. Any user can thenre-distribute the card or order the product displayed by tapping thecard 33, which takes the user to the screen shown in FIG. 1 e, fromwhich the user can order the product or re-send the card. The display oneach user's screen includes a “Reply” button 35 which when tapped willtake the user to the “Max” search screen shown in FIG. 1c to review theother search results or conduct a new search.

While the foregoing method illustrates the search for a product, thesame method also functions for searching for services, such as arestaurant. In the case of a restaurant service, selecting the “Order”button 25 once a search result card has been selected will take the userto a reservation page for the selected restaurant (such as “OpenTable”).

Thus the method allows the participant in a chat group to use a searchagent “Max” to locate a product or service, during the course of aconversation, and display a selected product or service to the group forordering. Thus a process of chatting, searching for a product/service,selecting the product/service from the search results and sharing theselected product/service into the chat group is provided. Until theAgent has a complete enough User Graph to automatically return requestedcontent directly into the chat itself, this more manual system ofsearch, select and share must be used to help build the User Graph andtrain the Agent. The system is monetized by obtaining a payment from thesource of the product or service when the product/service is ordered andpaid for from the vendor of the product/ service. In carrying out theforegoing activity, the User's searches, selections and sharing actionsfurther build out the User Graph, as described in detail below, everytime a search is performed.

User Interest Graph Structure, Building & Maintaining

In a more sophisticated embodiment, a number of the Users 10 on smartphones 10 provided with the necessary app register with Agent 22 overweb site 12 and provide certain common basic information about the Usersuch as age, sex, marital status, residential address, education etc.The User enters an agreement with the Agent to address privacy and otherissues to permit the Agent to collect information from the Userconcerning the User's location, searches and communications.

The Agent generates the User Graph for the registered user, startingwith the basic information provided on registration and collected overtime by the Agent which processes such information to curate, learn,query and predict things of use and interest to the User. Each UserGraph itself may comprise many subgraphs: event, search, social,interest, behavioral, biological, location, etc. Each of these subgraphsmay have its own specific set of applied properties and ontologies. TheUser Graph can be built and curated by the Agent through automatedcollection of in-context data and by direct entry from the Userthemselves to directly curate parts of their own User Graph orSubgraphs. An example of automated collection is the User's InterestGraph where in Facebook the User can like something. Another examplewould be to use the in-chat search app described above to map the User'ssearch, selection and sharing choices. The Agent collects and processesthe information to create and track the state of each of these subgraphsand may employ sub-agents or external services to format and updatethese subgraphs. This approach of subgraphing the User Graph providesflexibility in curating current in-use subgraph data collections withthe ability to add new subgraph types defined by new ontologies orprocesses without disruption to existing in-use subgraphs. For example,a User “Search Subgraph” that contains the User's solo or in-chatsearchs, selections and what they then shared, as described above, canbe created and updated by the Agent.

Preferably the User's relationship with the Agent starts before a chatwith others even begins, using features like search or a feed appdescribed below and others where the User and the Agent are workingtogether initially to build out the fully curated User Graph. The Agentinteracts and learns from the User and fills out subgraphs to the pointof curation. A trust relationship is developed between the User and theAgent. Helping the user work with the Agent to build out the other UserGraph subgraphs like the Event subgraph (planning/calendar), plugging inwearables data (heart rate, blood pressure, blood chemistry etc) intothe Biological subgraph, etc.

By using a particular notation, described herein as Simple KnowledgeGraph Notation (SKGN) to communicate data to the Agent, the User canhelp build higher density knowledge faster with less requiredcomputational resources. From the outset, the User may ‘chat’ with theAgent and tell them things and/or the Agent presents them with variousUser Interfaces (i.e. interest graph “Like”, “dislike”, “Loves”,“hates”, “Scary”, “Agrees” etc). This helps accelerate the building ofthe User Graph which in turn allows the Agent to do things and findthings of high value to the User. Initially the Agent may start as aslightly more useful search function for the User (than other searchengines), much like current search engines but as the User and Agentinteract by the filling out or collection of information to fill out theUser Graph, there will be a curve of convenience and reliance whereinthe User begins to interact more intimately with the Agent and there isan exponentially growing level of detail in the User Graph, which thenlevels off when the bulk of the user subgraphs are filled out and therate of density increase resembles a true machine prediction andcuration curve. This is illustrated in FIG. 2.

Currently other search agents play a passive role, analyzing the User'sactions and behavior behind the scenes and present the user withsuggestions based on probability-based lower density knowledge sets. Byusing SKGN the User can direct the Agent to higher density knowledgesets while utilizing less computational power, all producing much moreaccurate suggestions for the User.

User Graph Knowledge Density and its Value

High density knowledge sets in the User Graph are valuable because theyrepresent richer paths through knowledge space and thus capture moreaccurate user context, user state and subgraph state information.

As an example, a low density knowledge set would look like:(UserA)—[LIKES]—>(Beer)

A high density knowledge set would look like:

(UserA)—[LIKES]—>(Beer)—[TYPE]—>(Lager)—[BRANDED]—>(BeerWorksLager)—[PACKAGED]—>(Stubby Bottles)—[STATE]—>(chilled)

It is difficult to build high density knowledge sets using passivemethods as is currently done and costs more in processing time andanalysis to infer the same level of density, which may still suffer froma probability of failure. If the User formats the foregoing knowledgeset using one or more SKNG operator, the Agent can become much moreaccurate i.e. #beer>lager>chilled or #beerlagerchilled—the Agent will beable to leverage a higher density knowledge set to make more desirablesuggestion sets. This leverages the ability for the user to directlysupply the agent with personal explicit knowledge to use in the subgraphas contrasted to the agent filling the subgraph with inferredprobabilistic knowledge (i.e. bayesian).

Unlike other existing agents there are two coupled learning curves inaction: One for the Agent about the User and One for the User about theAgent. The Users themselves collect data/knowledge to build User GraphKnowledge Density (and for the Group State Graph a discussed below) bythe User being asked questions and taught how to format Knowledgeinformation and questions for the Agent (for example using SKGN).Existing systems scan through users' data, for example emails andsearches. Here active collection is done in parallel with backgroundNatural Language Processing and Inference. Here not only will the Agentreceive communications from the User to build the User Graph but alsothe User will receive communications from the Agent, such ascommunications in Simple Knowledge Graph Notation (SKGN) suggestingpaths of possible interest. The Users learn how to better communicatewith the Agent using SKGN. Hence there is a two-way dialogue.

Three Distinct Phases in the User-Agent Relationship Curve

a) Phase I: With specific reference to FIG. 2, in Phase I the User andAgent are just getting to know each other. User Graph (and subgraph)knowledge density is low. The Value to the user is similar to a Googlesearch with somewhat more usefulness because of Agent's basic knowledgeabout the User's gender or raw data like the User's geolocation.Typically in this phase, there is reliance on Natural LanguageProcessing (NLP) and Inference to build the User Graph withprobabilistic guesses. As the user selects specific suggestion tiles,those knowledge sets are added to the relevant User subgraph, thesubsequent Group State Graph (GSG) and to the Conversation subgraph.

b) Phase II: As the User begins to use more direct methods to activelydirect the Agent to specific tasks, the Agent is able to build accurate,high density knowledge in the User Graph. This gives the Agent betterdata for machine learning and conversely, it is able to ask the Usermore relevant questions and present more relevant suggestion tiles. Thismethodology of using SKGN in conjunction with other methods has anadvantage over existing approaches and creates an exponentially-slopedcurve simply because the User and Agent know how to talk to each otherand more efficiently transfer information between them. The SKGNnotational efficiency provides operational advantage over existing agentservices.

c) Phase III. Like any relationship, the amount of new information andknowledge set sharing will taper off and the emphasis will be on themaintenance and curation of the User Graph of knowledge sets. Once thispart of the curve has been reached, the ability for the Agent to predictaccurately what the User will want will be greater than existingapproaches because the Agent will have access to deeper value highdensity knowledge sets and the ability to curate them directly via theUser. This power will also be passed on to the GSG where the Agent(s)will be able to leverage group usage of SKGN to deliver more accuratesuggestion tiles.

User Case: The Feed App

As additional tools for assembling a deeper User Graph, a News Feed

App may be provided by allowing the Agent to chat directly with the user(and other UI components). In this context, there are no others involvedwith the User and the Agent and the Agent is engaging the three phaseswith the user to build the User Graph and to find interesting things onthe internet to build a continuous feed of items for the user toconsume, much like a Smart RSS feed. The User gives the Agent permissionto scan and pull information directly from the Facebook and Twitterfeeds as well as other sources like RSS news feeds. The User alsoinforms the Agent of topics and contexts of interest to build knowledgesets to help the Agent find and filter links to interesting content.

For example,

(UserA)—[LIKES]—>(Beer)—[TYPE]—>(Lager)—[BRANDED]—>(BeerWorksLager)—[PACKAGED]—>(Stubby Bottles)—[STATE]—>(chilled)

+

Beerworkslager>events>beergarden

Could produce a contextual Topic filter for the Feed like:

(UserA)—[LIKES]—>(Beer)—[TYPE]—>(Lager)—[BRANDED]—>(BeerWorksLager)—[EVENT]—>(BeerGarden)—[STATE]—>(chilled)

might lead to a link to relevant events within a geofence whereBeerWorks Lager is being served chilled, possibly in stubby bottles.

The user may take a resulting feed item like this and use it to INITIATEa chat with others.

For example, a Feed item has a (start chat) button on it which allowsthe user to select a few chat participants and initiates a chat with thefeed item at the top.

[Feed Item A]—>‘beerWorks Beer Garden Event’ (link)

[Feed Item A]—>Select Item Chat

[Contacts List]—>Select Group members

[Initiate and Invite to chat]

In Chat;

[link to Beer Garden Event]

[Hey guys! I found a Beer Garden gig this weekend where they are servingour fav BeerWorks Lager!]

In this way the Agent technology is involved from the outset, even beingused to seed the beginning of any conversation.

To be of practical usage, the User Graph with all subgraphs may be‘quantized’ into a number of “User State Graphs” which are a snapshot ofthe User Graph at a time (t) as illustrated in FIG. 3. For example thatanswers simple questions like “What did the user like last Wednesday?”,“What mood is the user in at the moment?”, “What do we think the userwill want to eat next Friday at lunch?” The visual representation of theUser State Graphs can be thought of as a slice across the time axisrevealing the User Graph and its subgraphs as a fixed entity-edge graphdiagram.

Leveraging the User Graph

This quantization of state is necessary for the Agent to be able toperform discrete analysis and processing of the User Graph or to handoff an anonymized User State Graph to other Agents for processing,conversation and transaction graph construction. Examples of User StateGraphs for Users A, B and C are shown in FIG. 8. User State Graphprocessing can be done by the Agent, sub-agents, affiliated Agents orarms length Agents or by a number of technologies to analyze it andperform valuable services such as (but not limited to) inferring theUser's likelihood of hunger, choosing and suggesting an appropriate mealor finding a meal purchase discount in advance and lining that up with amobile payment provider.

To perform its analysis, the Agent may use, but is not limited to, openontologies/taxonomies, folksonomies or private thesauri versions. TheAgent may use (but is not limited to) SKOS, OWL or OWL2 open sourceInference engines or private black box inference engines. The Agent mayuse (but is not limited to) statistical analysis to determine relevance,deep learning techniques between successive User State Graphs or NaturalLanguage Processing to augment User State Graph processing. FIG. 4illustrates User A's State Graphs at two times t1 and t2. Between thosetimes User A has eaten a spicy meal. The Agent Actions on the User Graphare shown, The Agent updates the User Graph at t2 based on the changeswhich occurred after t1. The Agent may then infer that the User needswater or medical assistance and presents suggestions to the User foraction. Finally the Agent updates the User Graph at time t3.

Group Chat Assembly

With reference to the flow chart in FIG. 5, this illustrates the processfor server 22 (or other server) to assemble a group chat among Users,even on different chat platforms. As the first step (1) Contacts: insidethe mobile native or web app, User A navigates to his list of contacts,shown in FIG. 6. This list is the aggregation of contacts pulled fromthe smart phone as well as various social networks. The entire listmight therefore contain a number of contacts for the same person.

At step 2 of FIG. 5, Contact Select, User A selects 1 or more contactsfrom the list that they want to start communications with. Thesecontacts could be from anywhere in the contact list and do not have tobe from the same chat provider. At step 3, Invite—User A now posts amessage into the newly created chat room. This message is now sent tothe server to allow it to deal with routing and user creation. At step4, Transport—information is sent to the server for processing.

At step 5 Routing—Server 22 uses a routing table to look up whichproviders User B and C are on. Once the server knows how to handle themessage it forwards the message on to be sent out by the messagehandlers. At step 6, Link—the server looks to construct a unique urlthat will allow the user A or B a temporary login to the chat. At step7, User Create—server 22 will find or create a “Pseudo” user accountthat will be used in the authentication of the link that is given out.The link is then sent using the proper provider to the users.

Those chat group members who are pseudo-users and do not register withthe Agent will not have a password to the system and may not have fullaccess to features in the system such as the ability to independentlyutilize the Agent outside of the chat group. The system assigns anaccount identifier for each pseudo-user referenced to the pseudo-user'semail, Facebook, Twitter, Linked-In, Xbox (or any other current orfuture provider's) account through which the user was invited into thechat group. One individual therefore may have multiple pseudo-useraccounts which eventually may become merged. The Agent will nonethelessbuild up the pseudo-user's User Graph as described below with whateverdata is available as it would for a regular user. Eventually thepseudo-user may register as a regular user, either after participatingin additional group chats, or when attempting to use the Agent. Varioususer interfaces can be provided at various intervals which willencourage the pseudo-user to register and log in.

In step 8, Acquisition—Users B and C click the link that was deliveredin the chat as illustrated in FIG. 7, The link will launch the browseron that device and begin loading the web chat. In step 9Authentication—the system uses the link to authenticate the “pseudo”user and move them into the web chat. Step 10 Unified Chat—Users are nowin a unified chat view as illustrated in FIG. 8 and are able toparticipate in the group chat and launch applications.

With reference to FIG. 9, User State Mapping, the Agent 22 now combinesthe group chat members' User State Graphs into a Group Graph. This canbe done by complete Union, or statistical relevance (e.g. “We alllike/love/crazy about Elvis”). This can also be done by simply workingout the intersecting commonalities in all the User Graphs andmaintaining those in the Group Graph as Group Common Interests (e.g.Everyone likes Lager and Spicy Food, Everyone dislikes Marching BandMusic). In step 2 of FIG. 10, the Group State Graph (GSG) is derivedfrom User State Graphs. This is used to process Group Chat activity andtrack it.

The process for generating a Group State Graph from User State Graphs isshown in FIG. 9. As noted above, the Agent has the task of monitoring,curating, and mapping the User State Graphs to a Group State Graphcontinuously over time. The User Stage Graph may be restricted torelevant subgraphs and not all subgraph members. The objective withGroup State Graph creation is not to lose any user information but alsoto map it into relevant subgraph information that can be acted onwithout referring to original user state graphs of group member users.Thus some computational inference and smart organization of the groupstate graph can be applied by the Agent to achieve this.

Referring to the illustration of User State Graph Mapping in FIG. 9, theAgent preserves information but at the same time can map and reduce theGroup State Graph. For example, as shown there is no need to preserveUser C's normal blood sugar level, only the outlier user subgraph forUsers A and B where the blood sugar levels are abnormal thus affecting asuggestion from the Agent. The same approach is shown in the example fora behavioral subgraph. Generally the Agent maps the union (overlap) ofthe User State Graphs but not necessarily the intersection ordifference, though that may be useful for some situations. In theexample shown, the Agent will normally want to keep the fact that User Cdislikes spicy meals but in this particular state, User C will not beeating so that might be inferred as redundant information for this GroupState Graph time slice, but would still be relevant to a later timeslice.

As the User State Graphs are updated, so is the Group State Graph whichis made up of time slices of mapped User State Graphs.

As an example of the Agent now utilizing the Conversation Graph whichresults from the conversation in FIG. 8 and as described below togenerate a suggestion, User A asks User B and User C if they have anyplans and would like to get together in the early evening for a while.The Agent can infer that because User C is not eating, a more casualdinning selection might be more relevant as a more formal dinner wouldmake User C feel uncomfortable. The Agent can infer that because User Bis not attending but also has friends at the hockey game, perhaps theAgent should see if there are any tickets available for User B if theyhave no other plans that evening.

Chat Entities and Conversation Subgraphs

In step (3) of FIG. 10, the Conversation Subgraph the building of whichis shown in FIG. 11, is added to the Group State Graph. The ConversationSubgraph is the Agent's notes obtained from the group conversation todate. It is obtained by the Agent through the users' use of a graphnotation in the chat messages (for example a Simple Knowledge GraphNotation or SKGN) and the Agent's analysis of it. The Conversation Graphmay or may not be maintained after a group ‘closes’. This may depend onwhether or not group is ad hoc or is named/saved. A Conversation Graphfor a chat session is constantly developing and added to as theconversation threads and branches. SKGN (or direct Agent involvement)sets a current focus state of the Conversation Graph so that incomingagent or vendor suggestions can be added to the conversation graph atthe focus topics. “Focus topics are shown as bold circle/ovals in FIG.11.

FIG. 11 illustrates the Conversation Subgraph & Agent Action Initiation.At the initiation of every chat, the Agent adds (if not already present,i.e. saved) a subgraph to the GSG called the Conversation Subgraph. TheConversation Subgraph is a mutual shared graph between all of the usersand agents in the chat. Like any social network conversation, there istypically a starting topic, that participants comment on and over thecourse of time, the comments and conversation tend to branch into newtopics related or relevant to the first topic. This bifurcation processis a natural part of human communication and is seen in most socialnetwork comments and instant messaging chats.

Every Group Graph can have one or more conversation subgraphsrepresenting different chats or conversations belonging to that group.At the beginning of every chat session, the agent chooses a pre-existingconversation subgraph or creates a new one for that Group Graph.Different conversation subgraphs can be accessible by some or all of thegroup members. Every Conversation Subgraph is composed of discrete chatentities, each of which represents a new entry into the ConversationSubgraph. A chat entity is a member of the graph database and can haveother entities and properties associated with it.

The chat entities are related to each other via a time sequence, aswould be found in a normal chat sequence of entries. Differentconversation subgraphs may be organized around users or topics and maynot use a temporal sequence per se. All conversation subgraphs capturecontext in the way their chat entities are created and organized whethersequentially or topic-wise.

Thus a given chat group may have multiple Conversation Subgraphs, witheach Conversation Subgraph directed to a particular area/topic. TheAgent can provide specialized application with customized userinterfaces which are focused/trained on particular topics e.g. hockey,gardening etc.

Like any social network conversation, there is typically a startingtopic, that then participants comment on and over the course of time,the comments and conversation tend to branch into different topicsrelated or relevant to the first topic. This bifurcation process is anatural part of human communication.

The User can invoke the Agent to perform different tasks from searchingfor things or doing other actions based on the User's request. These aretypically invoked by the use of SKGN, as shown in FIG. 11, by using the“#” and “&” operators in conjunction with a keyword.

In the example shown in FIG. 11, User C initiates an Agent search fortickets via “#tickets” to the event shared by User A. The Agent searchesfor tickets and then suggests ticket purchase options to User C todecide which to share with the rest of the group. In this case User Adecides that purchasing the tickets from TicketVendor3 is a good idea,they click the link presented in the Agent suggestion tile (shown as box“SUGGEST”) and are taken to TicketVendor3′s website to complete thetransaction for the ticket purchase. This may be by way of an Agent TileBuy (Action) button which invokes the agent to open a window/tab to avendor purchase screen (such as a ‘click here to purchase’ link below).

The Ticket purchase details are sent to User A′s email and that ticketinformation link can be shared with the rest of the group as shownbelow. In addition to sharing the ticket information link, User Adecides it would be a good idea to create a container (i.e. folder orbundle etc.) to attach the ticket info link to it as there will probablybe more information that needs to be organized for the event such asmaps, photos of the event etc. By using the “&WeekendEvent” SKGN, theAgent creates a container (if one doesn't exist) called “WeekendEvent”and adds the Music Event Link, ticket purchase info link to it,including additional links for the redemption of three free beers at theevent that were part of the ticket info web page. Note any of the otherUsers in the group chat can now add things to the group container.

Later in the chat, User B decides to search for an Uber cab to conveythe group to and from the Music Event. Again this is done using the SKGN#Uber hashtag and the append tag, “&WeekendEvent”. At any point, any ofthe users can click the Agent presented ‘WeekendEvent’ link and see adisplay of all the information in the container.

The primary goal of the conversation graph is to create a real timemappedgraph of the conversation so that the users and agents have a‘scaffold’ to make decisions with. The conversation graph ‘grows’ with athe addition of focus topics (shown in bold) that are added to theconversation graph from the chat entities, typically in context toprevious focus topics as shown in FIG. 11. Users can ‘vote’ on Agentsuggestion tiles with semantic context such as literal vote, like,dislike, agree etc. These contexts are also recorded in the conversationgraph as edge information between the focus topics and may allow theAgent to perform actions or add more focus topics to the conversationgraph. Every incoming chat entity message can be analyzed by NLP,Inference or other method and the result can be a probabilistic additionof more focus topics to the growing conversation graph.

By using SKGN, users can also directly grow the conversation graph, inthis case one topic node at a time via usage of “#” As users react toeach chat message or item introduced, they can add context to it and theagents can begin to infer possible actions to take or refine introducedsuggestions. As Users ask agents to perform actions (such as search,buy, book, reserve, sell etc.), this creates an ‘n-branch decision path’which is a subset of the conversation graph where decisions that lead toactions were made. In FIG. 9, these are represented by the heavieroutlined objects and dotted lines.

These decision paths are valuable in that they represent a ‘learned’group behavior set completely in context and relevance to that usergroup. These may be stored in the Group's Behavior graph (so the nexttime the group wants to drink beer, the Agent has 3 previous choices tooffer them) and also used in aggregate so that similar groups withsimilar context might also find them useful and actionable.

With reference back to FIG. 10, step (4), Transaction Graph, the Agentbuilds and maintains the Transaction Graph from the Group State Graph.The Transaction Graph acts as an anonymized, simplified version of theGSG that contains essential details about the group and the groupsdesires. Most subgraphs may be removed and the GSG conversation subgraphmay be normalized into strictly relevant purchase queries for Vendorsand/or Vendor Agents to consume for Product/Service Entity suggestions.

ConversationTopics & Simple Knowledge Graph Notation (SKGN); AgentConversation Knowledge Collection & Context/Relevance

Looking at step (5) in FIG. 10, Group Chat User Interface—Focus Topics,this is the Primary interface for chat participants. It uses, forexample the Simple Knowledge Graph Notation (SKGN) as described above todirectly invoke the Agent. For example the users use the symbol “#” todesignate a main topic of a chat and other symbols such as “>” and “.”may be used to designate additional context for the main topic. TheAgent may also self-invoke contextual data from the Users via NaturalLanguage Processing or Inference derived queues as well, based on theConversation Graph. The Agent curates the Conversation Graph as thegroup chat progresses and in turn also updates the GSG and TransactionGraphs. Users can set active focus topic nodes on the conversation graphby using the SKGN, and by returned suggestions from the Agent which get‘attached’ to the conversation. The group chat can have more than onefocus topic but even then may be in some relevant context (e.g. taxi,dinner & movie).

Agent Semantic Analysis & Product/Service Matching; AgentProduct/Service Selection from Context/Relevance; Agent Selection TileFormatting and Presentation; User Selection Tile In-Chat Presentation

Looking at step (6) in FIG. 10, Group Agents. On invoke, the Agentanalyses the GSG and draws Product/Service Entity suggestions fromProduct and Service Knowledge Graph database (using for example theGoogle Knowledge Graph). These are displayed as ‘suggestion tiles’ inthe Agent User Interface for the user to choose from. Once one or moretiles are selected, they are displayed in the Group Chat User Interfacefor the other chat members to consider. The User may also directlyinvoke a purchase/reservation directly from the Agent User Interfacewithout informing other chat members.

(7) & (8) Vendor & Vendor Agents. The Transaction Graph may also be madeavailable to Vendors who have registered with the system and theirVendor Agents 24. A Vendor who is interested in soliciting a groupthrough their Transaction Graph can do that themselves (8) or through amore automated fashion via their own agent 24. Vendor Agents can analysethe group's Transaction Graph and suggest products and services from theVendor's database.

Group Selection Tile Interaction & Feedback; Group Selection TileExecution & Conversion (Call to Action); Call to Action Tile in-ChatPresentation

Step (9) in FIG. 10 illustrates the Agent User Interface—Suggestion TileDisplay described above. The Agent User Interface displays all thesuggestions for the current conversation state from the group's agent(s)or the Vendor and/or their agents. The User selects a suggestion andthis gets added to the Conversation Graph focus node. Unselectedsuggestions can also be added to the focus node in different contexts. AUser can also directly engage a transaction with the suggested productor service without Group participation. A User can save any or allmembers of the suggestion set for future recall and/or engagement.

(10) Group Chat UI—Product Entity Selection. The User selections in step9 are displayed in the Group Chat User Interface. Other group memberscan then offer comment, vote or otherwise participate in the decision topurchase the suggested product of service.

(11) Purchase User Interface. The Purchase UI provides a transactionfunction by way of a payment system. Purchase UI may be hosted andoperated by a selected product vendor such as Paypal or may be handedoff to vendor site and the transaction completed outside of the Agentserver 22. Purchase is added to conversation graph as a purchasedentity.

(12) Fee Collection. A portion of the purchase transaction is creditedto the Agent as representing the system operator.

(13) Group Chat UI—Product Purchase Update. A Purchase Details (i.e.bundle) Tile is displayed in the Group Chat UI. The Purchase Tile issaveable/storable/retrievable by all group members. The Purchase Tilecan include a receipt, map, scannable barcode, and the like.

(14) Update Group Graph and GSG. At step 14 the Group Graph, GSG,Conversation and all other relevant graphs are updated.

While those chat group members who are pseudo-users will not have apassword to the system and may not have full access to features in thesystem such as the ability to independently utilize the Agent outside ofthe chat group, the Agent will have has generated a User Graph for eachpseudo-user referenced to the pseudo-user's email, Facebook, Twitter,Linked-In, Xbox etc. account through which the user was invited into thechat group. One individual therefore may have multiple pseudo-useraccounts which eventually can become merged. The Agent will build up thepseudo-user's User Graph with whatever data is available. Eventually thepseudo-user may register as a regular user, either after participatingin additional group chats, or when attempting to use the Agent throughuser interfaces which may be provided at various intervals to encouragethe pseudo-user to register and log in. There will further be anincentive for the pseudo-user to register with the system given that thepseudo-user will likely experience some of the benefits that come withthe knowledge acquisition which has been carried out by the Agent.

While a number of exemplary aspects and embodiments have been discussedabove, those of skill in the art will recognize certain modifications,permutations, additions and sub-combinations thereof. It is thereforeintended that the following appended claims and claims hereafterintroduced are interpreted to include all such modifications,permutations, additions and sub-combinations as are within their truescope.

What is claimed is:
 1. A computer-implemented method of using theInternet to connect merchants with potential purchasers in chat groupswho wish to obtain suitable sources of goods and services, wherein aplurality of chat group members each have a computer device comprisingmobile devices, tablets, smart devices, laptops or desktop computers forwirelessly accessing said computer network and provided with chatapplication software and software for accessing and interactivelycommunicating via a computer network with a server provided with asearch engine for searching the Internet, and wherein one or moremembers of said chat group is provided with application software on saidcomputer device for a software agent to maintain a User Graph for one ormore members of said chat group and a Group Graph combining two or moreUser Graphs, said method comprising: a) said software agent generatingone or more User Graphs for said one or more members of said chat group,each said User Graph comprising contextual data in relation to aspecific one of said one or more members of said chat group whereby saidsoftware agent tracks the state of the subject of each said User Graphby curating data to assist an understanding of said subject of said UserGraph's past and current states and needs and predict possible futurestates and needs; b) periodically initiating a chat conversation amongsaid plurality of chat group members wherein a search application forutilizing a search engine is invoked through said software agent, asearch of the Internet search for products or services is conductedduring the course of said chat conversation and one or more products orservices are ordered as a result of said Internet search; c) saidsoftware agent updating said one or more User Graphs to include the dataconcerning said Internet search and resulting order of products orservices; d) said software agent utilizing said User Graphs to narrowand focus the scope of searches by said chat group for products andservices.
 2. The method of claim 1 wherein said software agent convertseach User Graph into a plurality of User State Graphs to facilitate theanalysis of the User Graphs.
 3. The method of claim 2 wherein saidsoftware agent combines a plurality of said User State Graphs into aGroup State Graph to simultaneously curate an understanding of aplurality of chat group members' past and current states and predictpossible future states of said plurality of chat group members.
 4. Themethod of claim 1 wherein a unique form of notation is used forcommunication between each said chat group member and said softwareagent.
 5. The method of claim 4 wherein said unique form of notation isa Simple Knowledge Graph Notation.
 6. The method of claim 1 wherein saidsoftware agent generates a User Graph for users who join said chat groupfrom time to time without registering for the software agent's service,whereby said software agent can improve its ability to predict the needsand choices of the chat group.
 7. The method of claim 3 wherein saidsoftware agent generates a Conversation Subgraph added to the GroupState Graph to simultaneously curate an understanding of a plurality ofchat group members' past and current states and predict possible futurestates of said plurality of chat group members.